The 3D image dataset is intended to be free in particular of traces of at least one metal object imaged in at least some of the 2D image datasets. Such traces, customarily also referred to as artifacts, occur due to the way in which the 3D image dataset is calculated from 2D image datasets. In 2D image datasets, grayscale values are assigned to picture elements (pixels) in a two-dimensional image raster which correspond to pixels of e.g. an X-ray beam detector. In a 3D image dataset, grayscale values are assigned to voxels that are defined in the space filled by the image object, the grayscale values reflecting the extent to which the image object attenuates X-radiation in the region of the said voxel. The 3D image datasets are generated from the 2D image datasets by means of a 3D reconstruction operating e.g. according to the filtered back-projection principle. In this case it has been assumed that in each 2D image dataset the three-dimensional space has been projected in a specific manner into the image plane, making a back-projection possible. In a 2D X-ray image, a metal object leads to a particularly low grayscale value. In the back-projection of such a metal object on the basis of line integrals this is reflected not only in the fact that the grayscale values associated with the voxels which are occupied by the metal object also become correspondingly extremal, but also in that streak-shaped artifacts are present due to the back-projection principle. Said artifacts, also in the voxels which are occupied by soft parts of a typical image object, impede the interpretation of the reconstructed volume.
For this reason an attempt is made to remove not only the metal objects by computational means, but in particular also the associated artifacts. This is best accomplished by equalizing the corresponding grayscale values associated with the metal object already in the underlying 2D image datasets, in other words by eliminating the traces of the metal object. Said equalization is achieved in particular through interpolation. It is explicitly disclosed in DE 10 2007 016 319 A1, for example, according to which formula such an interpolation can be calculated. The interpolation causes substitute grayscale values (substitute data values) to be determined for individual pixels in the 2D image datasets. Outside of a specific region for the metal object, however, the 2D image datasets are preserved. A preprocessed 3D image dataset can then be generated on the basis of the thus preprocessed 2D image datasets.
It is evident, however, that such an interpolation and subsequent filtered back-projection (e.g. according to Feldkamp) nonetheless do not provide an assurance that the preprocessed 3D image dataset will be artifact-free.
DE 10 2007 016 319 A1 describes that such artifacts manifest themselves in the form of specific extrema in the grayscale values, so that by smoothing large gradients following the completion of the preprocessing it is possible to obtain a definitive three-dimensional reconstruction of the image object.
However, simply smoothing the large gradients on the basis of grayscale values assigned to pixels in their vicinity may lead to the loss of associated image information relating to the soft parts in the region of said large gradients which would otherwise be available.